(1) Field of Invention
The present invention relates to a method for flexible feature matching for object recognition in visual systems and, more particularly, to a method for flexible feature recognition in visual systems which incorporates evolutionary optimization.
(2) Description of Related Art
Feature matching is a process which involves matching feature points extracted from a sensed image to complementary points in a reference image. In existing approaches to feature matching, the features are defined as part of the classifier development or training process. Once the classifier is trained, the features become fixed or rigid in that they do not change or adapt to the input image. Biological vision systems do not have such rigidity. For example, humans recognize a car as a car even if parts of the car are displaced or rotated slightly with respect to each other. Existing computer vision systems must learn to handle such variations through training sets that include large numbers of variations. A human can generalize immediately without needing to see many such examples, in part, because of the flexible manner in which features are recognized. However, because existing computer vision systems must use large training sets, they are inflexible and generally less efficient.
Thus, a continuing need exists for a method for feature matching for object recognition that is flexible in the feature recognition or matching process and which results in better generalization without the requirement for large training sets to cover the range of possible variations.